Highway exit alert

ABSTRACT

A method for generating the at least one highway exit indicator, the method may include receiving video information and location information obtained during driving sessions of the plurality of vehicles; determining, based on the location information, multiple suspected highway exit events; selecting video information segments, wherein each selected video information segment is acquired before a suspected highway exit event and in timing proximity to the suspected highway exit event; and applying a machine learning process on at least some of the selected video information segments to find the at least one highway exit indicator.

BACKGROUND

Missing a highway exit is very common. Due to the high highway speedlimit, a driver may easily miss a highway exit. Due to the largedistance that may exist between consecutive highway exits this error maybe very costly.

In addition, a highway exit usually branches from one or more lanes ofthe highway and a driver that drives in another lane may not have enoughtime to reach the right lane—especially when the highway is relativelycrowded.

There is a growing need to reduce the chances of missing a highway exit.

SUMMARY

A method, system and non-transitory computer readable medium for highwayexit alert.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciatedmore fully from the following detailed description, taken in conjunctionwith the drawings in which:

FIG. 1A illustrates an example of a method;

FIG. 1B illustrates an example of a signature;

FIG. 1C illustrates an example of a dimension expansion process;

FIG. 1D illustrates an example of a merge operation;

FIG. 1E illustrates an example of hybrid process;

FIG. 1F illustrates an example of a method;

FIG. 1G illustrates an example of a method;

FIG. 1H illustrates an example of a method;

FIG. 1I illustrates an example of a method;

FIG. 1J illustrates an example of a method;

FIG. 1K illustrates an example of a method;

FIG. 1L illustrates an example of a method;

FIG. 1M illustrates an example of a system;

FIG. 1N is a partly-pictorial, partly-block diagram illustration of anexemplary obstacle detection and mapping system, constructed andoperative in accordance with embodiments described herein;

FIG. 1O illustrates an example of a method;

FIG. 1P illustrates an example of a method;

FIG. 1Q illustrates an example of a method;

FIG. 2A illustrates an example of a method;

FIG. 2B illustrates an example of a method;

FIG. 3 illustrates an example of an execution of a method; and

FIG. 4 illustrates an example of an execution of a method.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The specification and/or drawings may refer to an image. An image is anexample of a media unit. Any reference to an image may be appliedmutatis mutandis to a media unit. A media unit may be an example ofsensed information. Any reference to a media unit may be applied mutatismutandis to a natural signal such as but not limited to signal generatedby nature, signal representing human behavior, signal representingoperations related to the stock market, a medical signal, and the like.Any reference to a media unit may be applied mutatis mutandis to sensedinformation. The sensed information may be sensed by any type ofsensors—such as a visual light camera, or a sensor that may senseinfrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR(light detection and ranging), etc.

The specification and/or drawings may refer to a processor. Theprocessor may be a processing circuitry. The processing circuitry may beimplemented as a central processing unit (CPU), and/or one or more otherintegrated circuits such as application-specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), full-custom integratedcircuits, etc., or a combination of such integrated circuits.

Any combination of any steps of any method illustrated in thespecification and/or drawings may be provided.

Any combination of any subject matter of any of claims may be provided.

Any combinations of systems, units, components, processors, sensors,illustrated in the specification and/or drawings may be provided.

There may be provide a system, a method and a non-transitory computerreadable medium for providing a highway exit alert. The alert may begenerated a long time before the highway exit—for example at least 200,300, 400, 500, 700, 1000, 1300, 1600, 2000 meters—and even more.

FIG. 2A is an example of a method 101 for generating highway exitalerts.

Method 101 may be executed by a computerized system of a vehicle, by acomputerized systems of multiple vehicles (for example a bycollaboration, by load balancing, by allocating different tasks betweenthe different vehicles, and the like), by one or more computerizedsystems that does not belong to a vehicle, and the like.

Method 101 may start by step 110 of obtaining video information andlocation information obtained during driving sessions of one or morevehicles.

The location information is indicative of the locations in which thevideo information was acquired.

The location information may be GPS information or any locationinformation generated by the one or more vehicles.

The location information may be generated, at least in part, by otherentities—for example—the video information may be associated with timeof acquisition information, and the mapping between time of acquisitioninformation and location can be done by other entities—for examplehighway monitors that monitor time versus location of the one or morevehicles, may be provided, at least in part, by cellular networks orother network location determination entities, and the like.

Step 110 may be followed by step 120 of determining, based on thelocation information, and highway exits location information, multiplesuspected highway exit events.

The highway exits location information may be provided from any sourceand is indicative of locations of highway exits.

Step 120 may include finding video information parts that were acquiredat locations that overlap the locations of the highway exits.

Step 120 may also include selecting video information parts in which achange of direction—for example suspected turn of the vehicle was made.

Step 120 may include step 122 of verifying the multiple suspectedhighway exit events.

Step 122 may include tracking after a progress of a vehicle thatobtained the video information, during a suspected highway exit event.For example—check whether the vehicle deviated from the highway. Thismay include tracking after turns that may exceed an expected highwaycurvature, may include searching for a combination of significantdeceleration and turn of the vehicle, and the like.

Step 122 may include processing images of one or more wheels of thevehicle.

Step 120 may be followed by step 130 of selecting video informationsegments. Each selected video information segment is acquired before(for example a few seconds before, a half a minute before, a minutebefore, a few minutes before, few hundred meters before, one or morekilometers before) a suspected highway exit event and in timingproximity to the suspected highway exit event Timing proximity may meanthat the video information segment may end when reaching the suspectedhighway suspected highway exit event, a few seconds before reaching thesuspected highway exit event, a few second after reaching the suspectedhighway exit event, and the like.

Step 130 may be followed by step 140 of applying a machine learningprocess on at least some of the selected video information segments tofind the at least one highway exit indicator.

The selected video information segments may be acquired in relation tomultiple highway exits.

Per a certain highway exit—the machine learning process may generate oneor more highway exit indicators based on selected video informationsegment obtained only in relation to the certain highway exit.

Per a certain highway exit that belongs to a certain highway—the machinelearning process may generate one or more highway exit indicators basedon selected video information segment obtained only in relation to one,some or all highway exits of the certain highway.

Per a certain highway exit that belongs to a certain highway—the machinelearning process may generate one or more highway exit indicators basedon selected video information segment obtained only in relation to one,some or all highway exits of one or more highways.

Step 140 may include associating a distance between a highway exit andthe at least one highway exit indicator. The distance may be provided aspart of the highway exit alert.

A highway exit indicator may include image information (for example animage of a highway traffic sign, an image of a scenery before thehighway exit, an image of the highway exit, arrows or other symbolsformed on the highway itself, or a signature of said any of said imagesimage) and/or may include textual information (for example text thatshould appear in highway exit indicators, or a signature of such text).

Step 140 may include determining one or more lane of the highway fromwhich to initiate a highway exit process.

Step 140 may include generating a highway exit indicator that (a)appears or occurs at a first probability before and in time proximity toa highway exit and (b) appears or occurs outside a highway exit at asecond probability, the second probability is significantly lower (forexample at least 10, 15, 20, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75. 80,85, 90 percent) than the first probability.

FIG. 2B is an example of a method 102 for generating highway exitalerts.

Method 102 may be executed by a computerized system of a vehicle, by acomputerized systems of multiple vehicles (for example a bycollaboration, by load balancing, by allocating different tasks betweenthe different vehicles, and the like), by one or more computerizedsystems that does not belong to a vehicle, and the like.

Method 102 may start by step 110 of obtaining video information andlocation information obtained during driving sessions of one or morevehicles.

Step 110 may be followed by step 120 of determining, based on thelocation information, and highway exits location information, multiplesuspected highway exit events. Step 120 may include step 122.

Step 120 may be followed by step 130 of selecting video informationsegments. Each selected video information segment is acquired before asuspected highway exit event and in timing proximity to the suspectedhighway exit event.

Method 102 may also include step 135 of obtaining other videoinformation segments—that differ from the selected information segmentsof step 130. The other information segments are not acquired before asuspected highway exit event and in timing proximity to the suspectedhighway exit event.

One or more other video information segments may be one or moreunselected video information segments—video information segmentsobtained during step 110 but not selected during step 130.

One or more other video information segments may be obtained regardlessof the video information obtained during step 110. They may be obtainedfrom any source and/or in any manner.

Step 130 may be followed by step 141 of processing at least some of theselected video information segments and on at least some of the othervideo information segments to find the at least one highway exitindicator.

The processing may include applying the machine learning process on atleast the least some of the selected video information segments.

The processing may include applying the machine learning process on atleast the least some of the selected video information segments and onat least some of the other video information segments.

The processing may include applying one or more processing step inaddition to the machine learning process—after the machine learningprocess, before the machine learning process or during the machinelearning process.

Step 141 may be used to increase the distinctiveness of the at least onehighway exit indicator.

For example, step 141 may include:

-   -   Step 143 of obtaining first occurrence information regarding        occurrences of objects within the at least some of the selected        video information segments.    -   Step 145 of obtaining second occurrence information regarding        occurrences of objects within the at least some of the other        video information segments.    -   Step 147 of determining the at least one highway exit        identifier, based on the first occurrence information and the        second occurrence information. This may include selecting a        highway exit object that occurs much more at least some of the        selected video information segments than in the at least some        other video information segments. Much more—may be statistically        significant, or may provide any predefined or otherwise        requested other tradeoff between false positive and false        alarms.

The first occurrence information may be calculated based on theoccurrence of the objects within a combination of the least some of theselected video information segments and the at least some of the othervideo information segments.

FIG. 3 illustrates an example of a method 200 for highway exit alert.

Method 200 may start by step 210 of obtaining by a vehicle computerizedsystem, at least one highway exit indicator that is visible beforeexiting the highway.

Step 210 may include generating or receiving the at least one highwayexit indicator.

The at least one highway exit indicator may be generated using any oneof methods 101 and 102.

Step 210 may be followed by step 220 of obtaining sensed informationregarding an environment of the vehicle.

Step 220 may be followed by step 230 of processing the sensedinformation, wherein the processing may include searching a visualhighway exit indicator of the at least one highway exit indicator.

Step 230 may be followed by step 240 of determining whether the vehicleis reaching a driveway exit.

Step 240 may be followed by step 250 of generating the highway exitalert when determining that the vehicle is reaching the highway exit.

The alert may be visual, may be audio, may be audio visual, may includea tactile alert, may be indicative of the near highway exit, may providedistance information to the highway exit, may be generated severaltimes, may suggest a path to the highway exit, and the like.

The alert may be generated each highway exit, may be generated onlybefore highway exits that are including in a planned and/or suggestedpath of the vehicle, may be generated if the path has changed (forexample due to driving conditions), and the like.

Instructions and/or metadata for generating the alert may be provided instep 210 or may be generated and/or obtained in any other manner.

In relation to the processing of sensed and/or visual information and/orgeneration of signature—examples may be found, for example, in the textbelow and in PCT patent application WO2020/079508 which is incorporatedherein by reference.

FIG. 4 illustrates an example of an execution of a method.

FIG. 4 illustrates highway 300 having highway exit 302, two trafficsigns 303 and 304 with highway exit text, arrows 305 aimed to thehighway exit and to the rest of the highway are drawn on the highway.

A highway exit alert is generated when the vehicle senses at least oneof two traffic signs 303 and 304 and arrows 305.

FIG. 4 also illustrates a selected video information segment (such asone or more video clips) 321 obtained before (arrow 311) reaching thehighway exit and in timing proximity to the reaching of the highwayexit.

FIG. 4 also illustrates other visual information segment (such as one ormore video clips) 322 obtained after (arrow 312) reaching the highwayexit.

Low Power Generation of Signatures

The analysis of content of a media unit may be executed by generating asignature of the media unit and by comparing the signature to referencesignatures. The reference signatures may be arranged in one or moreconcept structures or may be arranged in any other manner. Thesignatures may be used for object detection or for any other use.

The signature may be generated by creating a multidimensionalrepresentation of the media unit. The multidimensional representation ofthe media unit may have a very large number of dimensions. The highnumber of dimensions may guarantee that the multidimensionalrepresentation of different media units that include different objectsis sparse—and that object identifiers of different objects are distantfrom each other—thus improving the robustness of the signatures.

The generation of the signature is executed in an iterative manner thatincludes multiple iterations, each iteration may include an expansionoperations that is followed by a merge operation. The expansionoperation of an iteration is performed by spanning elements of thatiteration. By determining, per iteration, which spanning elements (ofthat iteration) are relevant—and reducing the power consumption ofirrelevant spanning elements—a significant amount of power may be saved.

In many cases, most of the spanning elements of an iteration areirrelevant—thus after determining (by the spanning elements) theirrelevancy—the spanning elements that are deemed to be irrelevant may beshut down a/or enter an idle mode.

FIG. 1A illustrates a method 5000 for generating a signature of a mediaunit.

Method 5000 may start by step 5010 of receiving or generating sensedinformation.

The sensed information may be a media unit of multiple objects.

Step 5010 may be followed by processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationscomprises applying, by spanning elements of the iteration, dimensionexpansion process that are followed by a merge operation.

The processing may include:

Step 5020 of performing a k'th iteration expansion process (k may be avariable that is used to track the number of iterations).

Step 5030 of performing a k'th iteration merge process.

Step 5040 of changing the value of k.

Step 5050 of checking if all required iterations were done—if soproceeding to step 5060 of completing the generation of the signature.Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion results 5120.

The output of step 5030 is a k'th iteration merge results 5130.

For each iteration (except the first iteration)—the merge result of theprevious iteration is an input to the current iteration expansionprocess.

At least some of the K iterations involve selectively reducing the powerconsumption of some spanning elements (during step 5020) that are deemedto be irrelevant.

FIG. 1B is an example of an image signature 6027 of a media unit that isan image 6000 and of an outcome 6013 of the last (K'th) iteration.

The image 6001 is virtually segments to segments 6000(i,k). The segmentsmay be of the same shape and size but this is not necessarily so.

Outcome 6013 may be a tensor that includes a vector of values per eachsegment of the media unit. One or more objects may appear in a certainsegment. For each object—an object identifier (of the signature) pointsto locations of significant values, within a certain vector associatedwith the certain segment.

For example—a top left segment (6001(1,1)) of the image may berepresented in the outcome 6013 by a vector V(1,1) 6017(1,1) that hasmultiple values. The number of values per vector may exceed 100, 200,500, 1000, and the like.

The significant values (for example—more than 10, 20, 30, 40 values,and/or more than 0.1%, 0.2%. 0.5%, 1%, 5% of all values of the vectorand the like) may be selected. The significant values may have thevalues—but may eb selected in any other manner.

FIG. 1B illustrates a set of significant responses 6015(1,1) of vectorV(1,1) 6017(1,1). The set includes five significant values (such asfirst significant value SV1(1,1) 6013(1,1,1), second significant valueSV2(1,1), third significant value SV3(1,1), fourth significant valueSV4(1,1), and fifth significant value SV5(1,1) 6013(1,1,5).

The image signature 6027 includes five indexes for the retrieval of thefive significant values—first till fifth identifiers ID1-ID5 are indexesfor retrieving the first till fifth significant values.

FIG. 1C illustrates a k'th iteration expansion process.

The k'th iteration expansion process start by receiving the mergeresults 5060′ of a previous iteration.

The merge results of a previous iteration may include values areindicative of previous expansion processes—for example—may includevalues that are indicative of relevant spanning elements from a previousexpansion operation, values indicative of relevant regions of interestin a multidimensional representation of the merge results of a previousiteration.

The merge results (of the previous iteration) are fed to spanningelements such as spanning elements 5061(1)-5061(J).

Each spanning element is associated with a unique set of values. The setmay include one or more values. The spanning elements apply differentfunctions that may be orthogonal to each other. Using non-orthogonalfunctions may increase the number of spanning elements—but thisincrement may be tolerable.

The spanning elements may apply functions that are decorrelated to eachother—even if not orthogonal to each other.

The spanning elements may be associated with different combinations ofobject identifiers that may “cover” multiple possible media units.Candidates for combinations of object identifiers may be selected invarious manners—for example based on their occurrence in various images(such as test images) randomly, pseudo randomly, according to some rulesand the like. Out of these candidates the combinations may be selectedto be decorrelated, to cover said multiple possible media units and/orin a manner that certain objects are mapped to the same spanningelements.

Each spanning element compares the values of the merge results to theunique set (associated with the spanning element) and if there is amatch—then the spanning element is deemed to be relevant. If so—thespanning element completes the expansion operation.

If there is no match—the spanning element is deemed to be irrelevant andenters a low power mode. The low power mode may also be referred to asan idle mode, a standby mode, and the like. The low power mode is termedlow power because the power consumption of an irrelevant spanningelement is lower than the power consumption of a relevant spanningelement.

In FIG. 1C various spanning elements are relevant (5061(1)-5061(3)) andone spanning element is irrelevant (5061(J)).

Each relevant spanning element may perform a spanning operation thatincludes assigning an output value that is indicative of an identity ofthe relevant spanning elements of the iteration. The output value mayalso be indicative of identities of previous relevant spanning elements(from previous iterations).

For example—assuming that spanning element number fifty is relevant andis associated with a unique set of values of eight and four—then theoutput value may reflect the numbers fifty, four and eight—for exampleone thousand multiplied by (fifty+forty) plus forty. Any other mappingfunction may be applied.

FIG. 1C also illustrates the steps executed by each spanning element:

Checking if the merge results are relevant to the spanning element (step5091).

If—so—completing the spanning operation (step 5093).

If not—entering an idle state (step 5092).

FIG. 1D is an example of various merge operations.

A merge operation may include finding regions of interest. The regionsof interest are regions within a multidimensional representation of thesensed information. A region of interest may exhibit a more significantresponse (for example a stronger, higher intensity response).

The merge operation (executed during a k'th iteration merge operation)may include at least one of the following:

Step 5031 of searching for overlaps between regions of interest (of thek'th iteration expansion operation results) and define regions ofinterest that are related to the overlaps.

Step 5032 of determining to drop one or more region of interest, anddropping according to the determination.

Step 5033 of searching for relationships between regions of interest (ofthe k'th iteration expansion operation results) and define regions ofinterest that are related to the relationship.

Step 5034 of searching for proximate regions of interest (of the k'thiteration expansion operation results) and define regions of interestthat are related to the proximity Proximate may be a distance that is acertain fraction (for example less than 1%) of the multi-dimensionalspace, may be a certain fraction of at least one of the regions ofinterest that are tested for proximity.

Step 5035 of searching for relationships between regions of interest (ofthe k'th iteration expansion operation results) and define regions ofinterest that are related to the relationship.

Step 5036 of merging and/or dropping k'th iteration regions of interestbased on shape information related to shape of the k'th iterationregions of interest.

The same merge operations may applied in different iterations.

Alternatively, different merge operations may be executed duringdifferent iterations.

FIG. 1E illustrates an example of a hybrid process and an input image6001.

The hybrid process is hybrid in the sense that some expansion and mergeoperations are executed by a convolutional neural network (CNN) and someexpansion and merge operations (denoted additional iterations ofexpansion and merge) are not executed by the CNN—but rather by a processthat may include determining a relevancy of spanning elements andentering irrelevant spanning elements to a low power mode.

In FIG. 1E one or more initial iterations are executed by first andsecond CNN layers 6010(1) and 6010(2) that apply first and secondfunctions 6015(1) and 6015(2).

The output of these layers provided information about image properties.The image properties may not amount to object detection. Imageproperties may include location of edges, properties of curves, and thelike.

The CNN may include additional layers (for example third till N'th layer6010(N)) that may provide a CNN output 6018 that may include objectdetection information. It should be noted that the additional layers maynot be included.

It should be noted that executing the entire signature generationprocess by a hardware CNN of fixed connectivity may have a higher powerconsumption—as the CNN will not be able to reduce the power consumptionof irrelevant nodes.

FIG. 1F illustrates a method 7000 for low-power calculation of asignature.

Method 7000 starts by step 7010 of receiving or generating a media unitof multiple objects.

Step 7010 may be followed by step 7012 of processing the media unit byperforming multiple iterations, wherein at least some of the multipleiterations comprises applying, by spanning elements of the iteration,dimension expansion process that are followed by a merge operation.

The applying of the dimension expansion process of an iteration mayinclude (a) determining a relevancy of the spanning elements of theiteration; and (b) completing the dimension expansion process byrelevant spanning elements of the iteration and reducing a powerconsumption of irrelevant spanning elements until, at least, acompletion of the applying of the dimension expansion process.

The identifiers may be retrieval information for retrieving thesignificant portions.

The at least some of the multiple iterations may be a majority of themultiple iterations.

The output of the multiple iteration may include multiple propertyattributes for each segment out of multiple segments of the media unit;and wherein the significant portions of an output of the multipleiterations may include more impactful property attributes.

The first iteration of the multiple iteration may include applying thedimension expansion process by applying different filters on the mediaunit.

The at least some of the multiple iteration exclude at least a firstiteration of the multiple iterations. See, for example, FIG. 1E.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iterationmay be performed by the spanning elements of the iteration.

Method 7000 may include a neural network processing operation that maybe executed by one or more layers of a neural network and does notbelong to the at least some of the multiple iterations. See, forexample, FIG. 1E.

The at least one iteration may be executed without reducing powerconsumption of irrelevant neurons of the one or more layers.

The one or more layers may output information about properties of themedia unit, wherein the information differs from a recognition of themultiple objects.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative of an identity of the relevantspanning elements of the iteration. See, for example, FIG. 1C.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative a history of dimension expansionprocesses until the iteration that differs from the first iteration.

The each spanning element may be associated with a subset of referenceidentifiers. The determining of the relevancy of each spanning elementsof the iteration may be based a relationship between the subset of thereference identifiers of the spanning element and an output of a lastmerge operation before the iteration.

The output of a dimension expansion process of an iteration may be amultidimensional representation of the media unit that may include mediaunit regions of interest that may be associated with one or moreexpansion processes that generated the regions of interest.

The merge operation of the iteration may include selecting a subgroup ofmedia unit regions of interest based on a spatial relationship betweenthe subgroup of multidimensional regions of interest.

Method 7000 may include applying a merge function on the subgroup ofmultidimensional regions of interest. See, for example, FIG. 1C.

Method 7000 may include applying an intersection function on thesubgroup of multidimensional regions of interest. See, for example, FIG.1C.

The merge operation of the iteration may be based on an actual size ofone or more multidimensional regions of interest.

The merge operation of the iteration may be based on relationshipbetween sizes of the multidimensional regions of interest. Forexample—larger multidimensional regions of interest may be maintainedwhile smaller multidimensional regions of interest may be ignored of.

The merge operation of the iteration may be based on changes of themedia unit regions of interest during at least the iteration and one ormore previous iteration.

Step 7012 may be followed by step 7014 of determining identifiers thatare associated with significant portions of an output of the multipleiterations.

Step 7014 may be followed by step 7016 of providing a signature thatcomprises the identifiers and represents the multiple objects.

Localization and Segmentation

Any of the mentioned above signature generation method provides asignature that does not explicitly includes accurate shape information.This adds to the robustness of the signature to shape relatedinaccuracies or to other shape related parameters.

The signature includes identifiers for identifying media regions ofinterest.

Each media region of interest may represent an object (for example avehicle, a pedestrian, a road element, a human made structure,wearables, shoes, a natural element such as a tree, the sky, the sun,and the like) or a part of an object (for example—in the case of thepedestrian—neck, a head, an arm, a leg, a thigh, a hip, a foot, an upperarm, a forearm, a wrist, and a hand). It should be noted that for objectdetection purposes a part of an object may be regarded as an object.

The exact shape of the object may be of interest.

FIG. 1G illustrates method 7002 of generating a hybrid representation ofa media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multipleiterations, wherein at least some of the multiple iterations comprisesapplying, by spanning elements of the iteration, dimension expansionprocess that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multipleiterations, media unit regions of interest that contributed to theoutput of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein thehybrid representation may include (a) shape information regarding shapesof the media unit regions of interest, and (b) a media unit signaturethat includes identifiers that identify the media unit regions ofinterest.

Step 7024 may include selecting the media regions of interest persegment out of multiple segments of the media unit. See, for example,FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest. These polygonsmay be of a high degree.

In order to save storage space, the method may include step 7028 ofcompressing the shape information of the media unit to providecompressed shape information of the media unit.

FIG. 1H illustrates method 5002 for generating a hybrid representationof a media unit.

Method 5002 may start by step 5011 of receiving or generating a mediaunit.

Step 5011 may be followed by processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationscomprises applying, by spanning elements of the iteration, dimensionexpansion process that are followed by a merge operation.

The processing may be followed by steps 5060 and 5062.

The processing may include steps 5020, 5030, 5040 and 5050.

Step 5020 may include performing a k'th iteration expansion process (kmay be a variable that is used to track the number of iterations).

Step 5030 may include performing a k'th iteration merge process.

Step 5040 may include changing the value of k.

Step 5050 may include checking if all required iterations were done—ifso proceeding to steps 5060 and 5062. Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion result.

The output of step 5030 is a k'th iteration merge result.

For each iteration (except the first iteration)—the merge result of theprevious iteration is an input to the current iteration expansionprocess.

Step 5060 may include completing the generation of the signature.

Step 5062 may include generating shape information regarding shapes ofmedia unit regions of interest. The signature and the shape informationprovide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybridrepresentation, wherein the hybrid representation may include (a) shapeinformation regarding shapes of the media unit regions of interest, and(b) a media unit signature that includes identifiers that identify themedia unit regions of interest.

Object Detection Using Compressed Shape Information.

Object detection may include comparing a signature of an input image tosignatures of one or more cluster structures in order to find one ormore cluster structures that include one or more matching signaturesthat match the signature of the input image.

The number of input images that are compared to the cluster structuresmay well exceed the number of signatures of the cluster structures. Forexample—thousands, tens of thousands, hundreds of thousands (and evenmore) of input signature may be compared to much less cluster structuresignatures. The ratio between the number of input images to theaggregate number of signatures of all the cluster structures may exceedten, one hundred, one thousand, and the like.

In order to save computational resources, the shape information of theinput images may be compressed.

On the other hand—the shape information of signatures that belong to thecluster structures may be uncompressed—and of higher accuracy than thoseof the compressed shape information.

When the higher quality is not required—the shape information of thecluster signature may also be compressed.

Compression of the shape information of cluster signatures may be basedon a priority of the cluster signature, a popularity of matches to thecluster signatures, and the like.

The shape information related to an input image that matches one or moreof the cluster structures may be calculated based on shape informationrelated to matching signatures.

For example—a shape information regarding a certain identifier withinthe signature of the input image may be determined based on shapeinformation related to the certain identifiers within the matchingsignatures.

Any operation on the shape information related to the certainidentifiers within the matching signatures may be applied in order todetermine the (higher accuracy) shape information of a region ofinterest of the input image identified by the certain identifier.

For example—the shapes may be virtually overlaid on each other and thepopulation per pixel may define the shape.

For example—only pixels that appear in at least a majority of theoverlaid shaped should be regarded as belonging to the region ofinterest.

Other operations may include smoothing the overlaid shapes, selectingpixels that appear in all overlaid shapes.

The compressed shape information may be ignored of or be taken intoaccount.

FIG. 1I illustrates a matching process and a generation of a higheraccuracy shape information.

It is assumed that there are multiple (M) cluster structures4974(1)-4974(M). Each cluster structure includes cluster signatures,metadata regarding the cluster signatures, and shape informationregarding the regions of interest identified by identifiers of thecluster signatures.

For example—first cluster structure 4974(1) includes multiple (N1)signatures (referred to as cluster signatures CS) CS(1,1)-S(1,N1)4975(1,1)-4975(1,N1), metadata 4976(1), and shape information (Shapeinfo4977(1)) regarding shapes of regions of interest associated withidentifiers of the CSs.

Yet for another example—M'th cluster structure 4974(M) includes multiple(N2) signatures (referred to as cluster signatures CS) CS(M,1)-CS(M,N2)4975(M,1)-4975(M,N2), metadata 4976(M), and shape information (Shapeinfo4977(M)) regarding shapes of regions of interest associated withidentifiers of the CSs.

The number of signatures per concept structure may change over time—forexample due to cluster reduction attempts during which a CS is removedfrom the structure to provide a reduced cluster structure, the reducedstructure is checked to determine that the reduced cluster signature maystill identify objects that were associated with the (non-reduced)cluster signature—and if so the signature may be reduced from thecluster signature.

The signatures of each cluster structures are associated to each other,wherein the association may be based on similarity of signatures and/orbased on association between metadata of the signatures.

Assuming that each cluster structure is associated with a uniqueobject—then objects of a media unit may be identified by finding clusterstructures that are associated with said objects. The finding of thematching cluster structures may include comparing a signature of themedia unit to signatures of the cluster structures- and searching forone or more matching signature out of the cluster signatures.

In FIG. 1I—a media unit having a hybrid representation undergoes objectdetection. The hybrid representation includes media unit signature 4972and compressed shape information 4973.

The media unit signature 4972 is compared to the signatures of the Mcluster structures—from CS(1,1) 4975(1,1) till CS(M,N2) 4975(M,N2).

We assume that one or more cluster structures are matching clusterstructures.

Once the matching cluster structures are found the method proceeds bygenerating shape information that is of higher accuracy then thecompressed shape information.

The generation of the shape information is done per identifier.

For each j that ranges between 1 and J (J is the number of identifiersper the media unit signature 4972) the method may perform the steps of:

Find (step 4978(j)) the shape information of the j'th identifier of eachmatching signature- or of each signature of the matching clusterstructure.

Generate (step 4979(j)) a higher accuracy shape information of the j'thidentifier.

For example—assuming that the matching signatures include CS(1,1)2975(1,1), CS(2,5) 2975(2,5), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2),and that the j'th identifier is included in CS(1,1) 2975(1,1),CS(7,3)2975(7,3) and CS(15,2) 2975(15,2)—then the shape information of the j'thidentifier of the media unit is determined based on the shapeinformation associated with CS(1,1) 2975(1,1),CS(7,3) 2975(7,3) andCS(15,2) 2975(15,2).

FIG. 1P illustrates an image 8000 that includes four regions of interest8001, 8002, 8003 and 8004. The signature 8010 of image 8000 includesvarious identifiers including ID1 8011, ID2 8012, ID3 8013 and ID4 8014that identify the four regions of interest 8001, 8002, 8003 and 8004.

The shapes of the four regions of interest 8001, 8002, 8003 and 8004 arefour polygons. Accurate shape information regarding the shapes of theseregions of interest may be generated during the generation of signature8010.

FIG. 1J illustrates method 8030 for object detection.

Method 8030 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8030 may include a sequence of steps 8032, 8034, 8036 and 8038.

Step 8032 may include receiving or generating an input image.

Step 8034 may include generating a signature of the input image.

Step 8036 may include comparing the signature of the input image tosignatures of a certain concept structure. The certain concept structuremay be generated by method 8020.

Step 8038 may include determining that the input image comprises theobject when at least one of the signatures of the certain conceptstructure matches the signature of the input image.

FIG. 2D illustrates method 8040 for object detection.

Method 8040 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8040 may include a sequence of steps 8041, 8043, 8045, 8047 and8049.

Step 8041 may include receiving or generating an input image.

Step 8043 may include generating a signature of the input image, thesignature of the input image comprises only some of the certain secondimage identifiers; wherein the input image of the second scale.

Step 8045 may include changing a scale of the input image to the firstscale to a provide an amended input image.

Step 8047 may include generating a signature of the amended input image.

Step 8049 may include verifying that the input image comprises theobject when the signature of the amended input image comprises the atleast one certain first image identifier.

Object detection that is robust to angle of acquisition.

Object detection may benefit from being robust to the angle ofacquisition—to the angle between the optical axis of an image sensor anda certain part of the object. This allows the detection process to bemore reliable, use fewer different clusters (may not require multipleclusters for identifying the same object from different images).

FIG. 1K illustrates method 8120 that includes the following steps:

Step 8122 of receiving or generating images of objects taken fromdifferent angles.

Step 8124 of finding images of objects taken from different angles thatare close to each other. Close enough may be less than 1, 5, 10, 15 and20 degrees—but the closeness may be better reflected by the reception ofsubstantially the same signature.

Step 8126 of linking between the images of similar signatures. This mayinclude searching for local similarities. The similarities are local inthe sense that they are calculated per a subset of signatures. Forexample—assuming that the similarity is determined per two images—then afirst signature may be linked to a second signature that is similar tothe first image. A third signature may be linked to the second imagebased on the similarity between the second and third signatures- andeven regardless of the relationship between the first and thirdsignatures.

Step 8126 may include generating a concept data structure that includesthe similar signatures.

This so-called local or sliding window approach, in addition to theacquisition of enough images (that will statistically provide a largeangular coverage) will enable to generate a concept structure thatinclude signatures of an object taken at multiple directions.

Signature tailored matching threshold.

Object detection may be implemented by (a) receiving or generatingconcept structures that include signatures of media units and relatedmetadata, (b) receiving a new media unit, generating a new media unitsignature, and (c) comparing the new media unit signature to the conceptsignatures of the concept structures.

The comparison may include comparing new media unit signatureidentifiers (identifiers of objects that appear in the new media unit)to concept signature identifiers and determining, based on a signaturematching criteria whether the new media unit signature matches a conceptsignature. If such a match is found then the new media unit is regardedas including the object associated with that concept structure.

It was found that by applying an adjustable signature matching criteria,the matching process may be highly effective and may adapt itself to thestatistics of appearance of identifiers in different scenarios. Forexample—a match may be obtained when a relatively rear but highlydistinguishing identifier appears in the new media unit signature and ina cluster signature, but a mismatch may be declared when multiple commonand slightly distinguishing identifiers appear in the new media unitsignature and in a cluster signature.

FIG. 1L illustrates method 8200 for object detection.

Method 8200 may include:

Step 8210 of receiving an input image.

Step 8212 of generating a signature of the input image.

Step 8214 of comparing the signature of the input image to signatures ofa concept structure.

Step 8216 of determining whether the signature of the input imagematches any of the signatures of the concept structure based onsignature matching criteria, wherein each signature of the conceptstructure is associated within a signature matching criterion that isdetermined based on an object detection parameter of the signature.

Step 8218 of concluding that the input image comprises an objectassociated with the concept structure based on an outcome of thedetermining.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match. For example—assuming a signaturethat include few tens of identifiers, the minimal number may varybetween a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects andthat a signature of the input image may match multiple clusterstructures. Method 8200 is applicable to all of the matching processes-and that the signature matching criteria may be set for each signatureof each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signaturematching criterion by evaluating object detection capabilities of thesignature under different signature matching criteria.

Step 8202 may include:

Step 8203 of receiving or generating signatures of a group of testimages.

Step 8204 of calculating the object detection capability of thesignature, for each signature matching criterion of the differentsignature matching criteria.

Step 8206 of selecting the signature matching criterion based on theobject detection capabilities of the signature under the differentsignature matching criteria.

The object detection capability may reflect a percent of signatures ofthe group of test images that match the signature.

The selecting of the signature matching criterion comprises selectingthe signature matching criterion that once applied results in a percentof signatures of the group of test images that match the signature thatis closets to a predefined desired percent of signatures of the group oftest images that match the signature.

The object detection capability may reflect a significant change in thepercent of signatures of the group of test images that match thesignature. For example—assuming, that the signature matching criteria isa minimal number of matching identifiers and that changing the value ofthe minimal numbers may change the percentage of matching test images. Asubstantial change in the percentage (for example a change of more than10, 20, 30, 40 percent) may be indicative of the desired value. Thedesired value may be set before the substantial change, proximate to thesubstantial change, and the like.

For example, referring to FIG. 1I, cluster signatures CS(1,1), CS(2,5),CS(7,3) and CS(15,2) match unit signature 4972. Each of these matchesmay apply a unique signature matching criterion.

Examples of Systems

FIG. 1M illustrates an example of a system capable of executing one ormore of the mentioned above methods.

The system include various components, elements and/or units.

A component element and/or unit may be a processing circuitry may beimplemented as a central processing unit (CPU), and/or one or more otherintegrated circuits such as application-specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), full-custom integratedcircuits, etc., or a combination of such integrated circuits.

Alternatively, each component element and/or unit may implemented inhardware, firmware, or software that may be executed by a processingcircuitry.

System 4900 may include sensing unit 4902, communication unit 4904,input 4911, one or more processors—such as processor 4950, and output4919. The communication unit 4904 may include the input and/or theoutput. The communication unit 4904 may communicate with anyentity—within the vehicle (for example driver device, passenger device,multimedia device), outside the vehicle (another vehicle, anothercomputerized system—such as out-of-vehicle computerized system 4820 ofFIG. 1N, another road user, another human outside the vehicle), and thelike.

Input and/or output may be any suitable communications component such asa network interface card, universal serial bus (USB) port, disk reader,modem or transceiver that may be operative to use protocols such as areknown in the art to communicate either directly, or indirectly, withother elements of the system.

Processor 4950 may include at least some out of (and thus may notinclude at least one out of):

-   -   Multiple spanning elements 4951(q).    -   Multiple merge elements 4952(r).    -   Object detector 4953.    -   Cluster manager 4954.    -   Controller 4955.    -   Selection unit 4956.    -   Object detection determination unit 4957.    -   Signature generator 4958.    -   Movement information unit 4959.    -   Identifier unit 4960.

While system 4900 includes a sensing unit 4902—is should be noted thatit may receive sensed information from other sensors and/or that thesensing unit does not belong to the system. The system may receiveinformation from one or more sensors located in the vehicle, associatedwith the vehicle, and/or located outside the vehicle.

Any method illustrated in the specification may be fully or partiallyexecuted by system 4900, and/or may be fully or partially executed byone or more other computerized system, and/or by one or morecomputerized systems—for example by task allocations betweencomputerized systems, by a cooperation (for example—exchange ofinformation, exchange of decisions, any allocation of resources,collaborative decision, and the like) between multiple computerizedsystems.

The one or more other computerized systems may be, for example,out-of-vehicle computerized system 4820 of FIG. 1N, any otherout-of-vehicle computerized system, one or more other in-vehiclesystems, a computerized device of a person within the vehicle, anycomputerized system outside the vehicle—including for example acomputerized system of another vehicle.

An example of an other in-vehicle system is denoted 4830 in FIG. 1N andis located within vehicle 4800 that drives along road 4820.

System 4900 may obtain sensed information from any type of sensors—acamera, one or more sensors implemented using any suitable imagingtechnology instead of, or in addition to, a conventional camera, aninfrared sensor, a radar, an ultrasound sensor, any electro-opticsensor, a radiography sensor, a LIDAR (light detection and ranging),telemetry ECU sensor, shock sensor, etc.

System 4900 and/or other in-vehicle system is denoted 4830 may usesupervised and/or unsupervised learning to perform any method executedby them.

The other in-vehicle system 4830 may be an autonomous driving system, anadvance driver assistance system, or may differ from an autonomousdriving system and from an advance driver assistance system.

The other in-vehicle system 4830 may include processing circuitry 210,input/output (I/O) module 220, one or more sensors 233, and database270. The processing circuitry 210 may execute any task is it assigned orprogrammed to perform in relation to any of the methods illustrate dinthe application. Alternatively—the other in-vehicle system 4830 mayinclude another module for executing (alone or with the processingcircuit) any such task. For example—the processing circuitry may executeinstructions to provide an autonomous driving manager functionality.Alternatively—another circuit or module of the in-vehicle system 4830may provide the autonomous driving manager functionality.

FIG. 1O illustrates method 7002 of generating a hybrid representation ofa media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multipleiterations, wherein at least some of the multiple iterations comprisesapplying, by spanning elements of the iteration, dimension expansionprocess that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multipleiterations, media unit regions of interest that contributed to theoutput of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein thehybrid representation may include (a) shape information regarding shapesof the media unit regions of interest, and (b) a media unit signaturethat includes identifiers that identify the media unit regions ofinterest.

Step 7024 may include selecting the media regions of interest persegment out of multiple segments of the media unit. See, for example,FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest. These polygonsmay be of a high degree.

In order to save storage space, the method may include step 7028 ofcompressing the shape information of the media unit to providecompressed shape information of the media unit.

FIG. 1P illustrates method 8020 for scale invariant object detection.

Method 8020 may include a first sequence of steps that may include step8022, 8024, 8026 and 8028.

Step 8022 may include receiving or generating a first image in which anobject appears in a first scale and a second image in which the objectappears in a second scale that differs from the first scale.

Step 8024 may include generating a first image signature and a secondimage signature.

The first image signature includes a first group of at least one certainfirst image identifier that identifies at least a part of the object.

The second image signature includes a second group of certain secondimage identifiers that identify different parts of the object.

The second group is larger than first group—as the second group has moremembers than the first group.

Step 8026 may include linking between the at least one certain firstimage identifier and the certain second image identifiers.

Step 8026 may include linking between the first image signature, thesecond image signature and the object.

Step 8026 may include adding the first signature and the secondsignature to a certain concept structure that is associated with theobject.

Step 8028 may include determining whether an input image includes theobject based, at least in part, on the linking. The input image differsfrom the first and second images.

The determining may include determining that the input image includesthe object when a signature of the input image includes the at least onecertain first image identifier or the certain second image identifiers.

The determining may include determining that the input image includesthe object when the signature of the input image includes only a part ofthe at least one certain first image identifier or only a part of thecertain second image identifiers.

The linking may be performed for more than two images in which theobject appears in more than two scales.

FIG. 1Q illustrates method 8200 for object detection.

Method 8200 may include:

Step 8210 of receiving an input image.

Step 8212 of generating a signature of the input image.

Step 8214 of comparing the signature of the input image to signatures ofa concept structure.

Step 8216 of determining whether the signature of the input imagematches any of the signatures of the concept structure based onsignature matching criteria, wherein each signature of the conceptstructure is associated within a signature matching criterion that isdetermined based on an object detection parameter of the signature.

Step 8218 of concluding that the input image comprises an objectassociated with the concept structure based on an outcome of thedetermining.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match. For example—assuming a signaturethat include few tens of identifiers, the minimal number may varybetween a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects andthat a signature of the input image may match multiple clusterstructures. Method 8200 is applicable to all of the matchingprocesses—and that the signature matching criteria may be set for eachsignature of each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signaturematching criterion by evaluating object detection capabilities of thesignature under different signature matching criteria.

Step 8202 may include:

Step 8203 of receiving or generating signatures of a group of testimages.

Step 8204 of calculating the object detection capability of thesignature, for each signature matching criterion of the differentsignature matching criteria.

Step 8206 of selecting the signature matching criterion based on theobject detection capabilities of the signature under the differentsignature matching criteria.

The object detection capability may reflect a percent of signatures ofthe group of test images that match the signature.

The selecting of the signature matching criterion comprises selectingthe signature matching criterion that once applied results in a percentof signatures of the group of test images that match the signature thatis closets to a predefined desired percent of signatures of the group oftest images that match the signature.

The object detection capability may reflect a significant change in thepercent of signatures of the group of test images that match thesignature. For example—assuming, that the signature matching criteria isa minimal number of matching identifiers and that changing the value ofthe minimal numbers may change the percentage of matching test images. Asubstantial change in the percentage (for example a change of more than10, 20, 30, 40 percent) may be indicative of the desired value. Thedesired value may be set before the substantial change, proximate to thesubstantial change, and the like.

Any reference in the specification to a method should be applied mutatismutandis to a system capable of executing the method and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that once executed by a computer result in theexecution of the method.

Any reference in the specification to a system and any other componentshould be applied mutatis mutandis to a method that may be executed by asystem and should be applied mutatis mutandis to a non-transitorycomputer readable medium that stores instructions that may be executedby the system.

Any reference in the specification to a non-transitory computer readablemedium should be applied mutatis mutandis to a system capable ofexecuting the instructions stored in the non-transitory computerreadable medium and should be applied mutatis mutandis to method thatmay be executed by a computer that reads the instructions stored in thenon-transitory computer readable medium.

Any combination of any module or unit listed in any of the figures, anypart of the specification and/or any claims may be provided. Especiallyany combination of any claimed feature may be provided.

Any reference to the term “comprising” or “having” should be interpretedalso as referring to “consisting” of “essentially consisting of”. Forexample—a method that comprises certain steps can include additionalsteps, can be limited to the certain steps or may include additionalsteps that do not materially affect the basic and novel characteristicsof the method—respectively.

The invention may also be implemented in a computer program for runningon a computer system, at least including code portions for performingsteps of a method according to the invention when run on a programmableapparatus, such as a computer system or enabling a programmableapparatus to perform functions of a device or system according to theinvention. The computer program may cause the storage system to allocatedisk drives to disk drive groups.

A computer program is a list of instructions such as a particularapplication program and/or an operating system. The computer program mayfor instance include one or more of: a subroutine, a function, aprocedure, an object method, an object implementation, an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system.

The computer program may be stored internally on a computer programproduct such as non-transitory computer readable medium. All or some ofthe computer program may be provided on non-transitory computer readablemedia permanently, removably or remotely coupled to an informationprocessing system. The non-transitory computer readable media mayinclude, for example and without limitation, any number of thefollowing: magnetic storage media including disk and tape storage media;optical storage media such as compact disk media (e.g., CD-ROM, CD-R,etc.) and digital video disk storage media; nonvolatile memory storagemedia including semiconductor-based memory units such as FLASH memory,EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatilestorage media including registers, buffers or caches, main memory, RAM,etc. A computer process typically includes an executing (running)program or portion of a program, current program values and stateinformation, and the resources used by the operating system to managethe execution of the process. An operating system (OS) is the softwarethat manages the sharing of the resources of a computer and providesprogrammers with an interface used to access those resources. Anoperating system processes system data and user input, and responds byallocating and managing tasks and internal system resources as a serviceto users and programs of the system. The computer system may forinstance include at least one processing unit, associated memory and anumber of input/output (I/O) devices. When executing the computerprogram, the computer system processes information according to thecomputer program and produces resultant output information via I/Odevices.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturesmay be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality may be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented assoft or code representations of physical circuitry or of logicalrepresentations convertible into physical circuitry, such as in ahardware description language of any appropriate type.

Also, the invention is not limited to physical devices or unitsimplemented in non-programmable hardware but can also be applied inprogrammable devices or units able to perform the desired devicefunctions by operating in accordance with suitable program code, such asmainframes, minicomputers, servers, workstations, personal computers,notepads, personal digital assistants, electronic games, automotive andother embedded systems, cell phones and various other wireless devices,commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

What is claimed is:
 1. A method for highway exit alert, the methodcomprises: receiving by a vehicle computerized system, at least onehighway exit indicator that is visible before exiting the highway;obtaining sensed information regarding an environment of the vehicle;processing the sensed information, wherein the processing comprisessearching a visual highway exit indicator of the at least one highwayexit indicator; determine whether the vehicle is reaching a drivewayexit; and generating the highway exit alert when determining that thevehicle is reaching the highway exit.
 2. The method according to claim 1wherein the at least one highway exit indicator is generated by:receiving video information and location information obtained duringdriving sessions of the plurality of vehicles; determining, based on thelocation information, multiple suspected highway exit events; selectingvideo information segments, wherein each selected video informationsegment is acquired before a suspected highway exit event and in timingproximity to the suspected highway exit event; and applying a machinelearning process on at least some of the selected video informationsegments to find the at least one highway exit indicator.
 3. The methodaccording to claim 1 comprising generating the at least one highway exitindicator by: receiving video information and location informationobtained during driving sessions of the plurality of vehicles;determining, based on the location information, multiple suspectedhighway exit events; selecting video information segments, wherein eachselected video information segment is acquired before a suspectedhighway exit event and in timing proximity to the suspected highway exitevent; and applying a machine learning process on at least some of theselected video information segments to find the at least one highwayexit indicator.
 4. The method according to claim 3 comprising applyingmultiple machine learning processes on multiple groups of the selectedvideo information segments, one group per suspected highway exit.
 5. Themethod according to claim 3 comprising applying the machine learningprocess on selected video information segments associated with more thanone highway exit.
 6. The method according to claim 3 comprisingverifying the multiple suspected highway exit events.
 7. The methodaccording to claim 6 wherein the verifying of the multiple suspectedhighway exit events comprising tracking after a progress of a vehicle,of the plurality of vehicle, during a suspected driving event.
 8. Themethod according to claim 7 wherein the tracking after the progress ofthe vehicle, of the plurality of vehicle, during the suspected drivingevent comprises processing images of one or more wheels of the vehicle.9. The method according to claim 3 comprising associating a distancebetween a highway exit and the at least one highway exit indicator. 10.The method according to claim 3 wherein the at least one highway exitindicator comprises at least one out of an image information and textualinformation.
 11. The method according to claim 3 comprising determiningone or more lane of the highway from which to initiate a highway exitprocess.
 12. A non-transitory computer readable medium for highway exitalert, the non-transitory computer readable medium stores instructionsfor: receiving by a vehicle computerized system, at least one highwayexit indicator that is visible before exiting the highway; obtainingsensed information regarding an environment of the vehicle; processingthe sensed information, wherein the processing comprises searching avisual highway exit indicator of the at least one highway exitindicator; determine whether the vehicle is reaching a driveway exit;and generating the highway exit alert when determining that the vehicleis reaching the highway exit.
 13. A method for providing at least onehighway exit indicator, the method comprises: obtaining videoinformation and location information obtained during driving sessions ofone or more vehicles; determining, based on the location information,and highway exits location information, multiple suspected highway exitevents; selecting video information segments, each selected videoinformation segment is acquired before a suspected highway exit eventand in timing proximity to the suspected highway exit event; obtainingother video information segments that differ from the selectedinformation segments; and processing at least some of the selected videoinformation segments and on at least some of the other video informationsegments to find the at least one highway exit indicator;
 14. The methodaccording to claim 13 wherein the processing comprises applying themachine learning process on at least the least some of the selectedvideo information segments.
 15. The method according to claim 13 whereinthe processing comprises applying the machine learning process on atleast the least some of the selected video information segments and onat least some of the other video information segments.
 16. The methodaccording to claim 13 wherein the processing comprises obtaining firstoccurrence information regarding occurrences of objects within the atleast some of the selected video information segments; obtaining secondoccurrence information regarding occurrences of objects within the atleast some of the other video information segments; and determining theat least one highway exit identifier, based on the first occurrenceinformation and the second occurrence information.
 17. The methodaccording to claim 13 wherein the obtaining comprises receiving, from aplurality of vehicles, and by an I/O module of a computerized system,the video information and the location information obtained duringdriving sessions of the plurality of vehicles.